Textual Analysis in Accounting and Finance: A Survey: TEXTUAL ANALYSIS IN ACCOUNTING AND FINANCE

By Tim Loughran, Bill McDonald. 2016

1,458 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, neutral): “[161], Kumar et al.”
Natural language based financial forecasting: a survey (2017, background): “In [72], they do not attempt to evaluate the performance of a built system, but focus more on introducing resources used and interpretability.”
Readability of Narrative Disclosures in 10-K Reports: Does Managerial Ability Matter? (2018, positive): “Prior studies use linguistic analysis tools to examine the causes and consequences of the readability of narrative disclosures (Ertugrul, Lei, Qiu, & Wan, 2017; Li, 2008; Lo, Ramos, & Rogo, 2017; Loughran & McDonald, 2016).”
Climate Change News Risk and Corporate Bond Returns (2020, positive): “Loughran and McDonald (2016) provide an excellent survey on textual analysis in accounting and finance. errors clustered at the firm level, making the estimated coefficients and standard errors more robust than those obtained from the traditional Fama–MacBeth framework.16 Table 2 reports the…”
FinBERT: Financial Sentiment Analysis with Pre-trained Language Models (2019, background): “Loughran and McDonald (2016) presents a thorough survey of recent works on financial text analysis utilizing machine learning with "bag-of-words" approach or lexicon-based methods [12].”
The Impact of Audit Committee Financial Expertise on Management Discussion and Analysis (MD&A) Tone (2019, methodology): “Adjusting the list because of these misclassified words, Loughran and McDonald (2011) provide a more appropriate dictionary for analysis of financial”
Saying more with less? Disclosure conciseness, completeness and balance in Integrated Reports (2017, positive): “…by developing a measurement approach that draws on both the IIRC Framework (IIRC, 2013) and extant accounting studies applying textual analysis of narrative disclosures in financial reporting (cf. De Franco et al., 2015; Lang and Stice-Lawrence, 2015; Li, 2008, 2010; Loughran and McDonald, 2016).”
The Economics of Disclosure and Financial Reporting Regulation: Evidence and Suggestions for Future Research (2015, methodology): “By aggregating across measures, these studies attempt to obtain a less specific (or summary) measure 19 See also Das (2014) and Loughran and McDonald (2014, 2016).”
Intraday online investor sentiment and return patterns in the U.S. stock market (2017, background): “…term-weighting has the potential to increase the accuracy of textual analysis, the large number of available weighting procedures may give too many degrees of freedom to researchers in selecting the best possible empirical specification (Loughran and McDonald, 2016), creating a risk of overfitting.”
Disclosure on the Sustainable Development Goals – Evidence from Europe (2021, positive): “Textual analysis in general refers to the ‘notion of parsing text for patterns’ (Loughran & McDonald, 2016, p. 1187).”
Measuring Corporate Culture Using Machine Learning (2020, background): “The disadvantages of doing so include the imprecision associated with those modules (Loughran and McDonald 2016) and extra computational time.”